To show the effectiveness of our proposed method, we gradually add a component while keeping the rest unchanged. To see the gain of each component, we train a baseline network consists of four PVC layers. The baseline does not use local feature aggregation, instead an averaged feature of all points fall into the same voxel is taken. Fusion method is summation only. And no channel attention module(CAM) is used for prediction. Final prediction is directly produced from global feature without deep supervision. Experiments are conducted on ShapeNetPart[1]}. The baseline model is the compact version (\(0.5\times C\) ). From Table REF , we can see that each component is able to boost baseline method without necessarily increase latency too much.
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